**README** Here, you will find all data necessary to recreate the figures from the main manuscript and the supplement concerning reproduction of the study results on electronic health records data from TriNetX Analytics. Each RDS file contains an R variable which consists of the result for the survival analysis comparing two cohorts. Specifically: ** OSF_high_vs_low_fibrinogen_COVID.rds** - containing the results for the comparison between hospitalised COVID-19 patients with **high** fibrinogen and CRP ≤ 10mg/L and hospitalised COVID-19 patients with **low** fibrinogen and CRP ≤ 10 mg/L. **OSF_high_vs_low_ddimer_COVID.rds** - containing the results for the comparison between hospitalised COVID-19 patients with **high** D-dimer and CRP ≤ 10mg/L and hospitalised COVID-19 patients with **low** D-dimer and CRP ≤ 10 mg/L. **OSF_high_vs_low_fibrinogen_COVID_CRP20.rds** - containing the results for the comparison between hospitalised COVID-19 patients with **high** fibrinogen and CRP ≤ 20mg/L and hospitalised COVID-19 patients with **low** fibrinogen and CRP ≤ 20 mg/L. **OSF_high_vs_low_ddimer_COVID_CRP20.rds** - containing the results for the comparison between hospitalised COVID-19 patients with **high** D-dimer and CRP ≤ 20mg/L and hospitalised COVID-19 patients with **low** D-dimer and CRP ≤ 20 mg/L. **OSF_high_vs_low_fibrinogen_COVID_CRPany.rds** - containing the results for the comparison between hospitalised COVID-19 patients with **high** fibrinogen and with CRP taking any value and hospitalised COVID-19 patients with **low** fibrinogen and with CRP taking any value. **OSF_high_vs_low_ddimer_COVID_CRPany.rds** - containing the results for the comparison between hospitalised COVID-19 patients with **high** D-dimer and with CRP taking any value and hospitalised COVID-19 patients with **low** D-dimer and with CRP control. **OSF_high_vs_low_fibrinogen_nonCOVID.rds** - containing the results for the comparison between hospitalised non-COVID-19 patients with **high** fibrinogen and hospitalised non-COVID-19 patients with **low** fibrinogen. **OSF_high_vs_low_ddimer_nonCOVID.rds** - containing the results for the comparison between hospitalised non-COVID-19 patients with **high** D-dimer and hospitalised non-COVID-19 patients with **low** D-dimer. **OSF_COVID_vs_nonCOVID_high_ddimer.rds** - containing the result for the comparison between hospitalised **COVID-19** patients with high D-dimer and hospitalised **non-COVID-19** patients with high D-dimer. Once loaded, the variable contains a list with the following fields: **“outcomeNames”**: a vector of outcome names. For all files but **OSF_COVID_vs_nonCOVID_high_ddimer.rds**, this is only “Cognitive deficit (first)”; for **OSF_COVID_vs_nonCOVID_high_ddimer.rds** outcomes names are “VTE (first)” and "Ischaemic stroke (first)". You can navigate across different outcomes by indexing, e.g. "Ischaemic stroke (first)" in **OSF_COVID_vs_nonCOVID_high_ddimer.rds** has index #2. **“outcomes”**: a list of the same length as “outcomeNames” which contains the results for each outcome (i.e. it is a list of lists). For instance, “outcomes[[2]]” in **OSF_COVID_vs_nonCOVID_high_ddimer.rds** file contains the results for ischaemic stroke. Each outcome contains the following fields: - **“HR”**: the HR at 6 months - **“HR_CI”**: the 95% CI of the HR at 6 months - **“HR_p”**: the p-value of the HR at 6 months - **“KM”**: a list that contains the data for the Kaplan-Meier curves and its confidence interval **“cohort1”**: the name of the first cohort **“cohort2”**: the name of the second cohort As an illustration, the following lines of code would plot the KM curves for cognitive deficit (outcome #1) in the comparison between the hospitalised COVID-19 cohort with high fibrinogen and CRP ≤ 10mg/L and a hospitalised COVID-19 cohort with low fibrinogen and CRP ≤ 10mg/L (corresponding to Figure 5A of the main manuscript without the 95% CI and without the style): `res=readRDS(‘OSF_high_vs_low_fibrinogen_COVID.rds’)` `plot(res$outcomes[[1]]$KM$time,1-res$outcomes[[1]]$KM$values2,type='l')` `lines(res$outcomes[[1]]$KM$time,1-res$outcomes[[1]]$KM$values1,lty=2)` Note that the KM values are stored as survival probability (hence the need to calculate 1-value to get the incidence as in Fig. 5A).